Prediction of mechanical properties of A357 alloy using artificial neural network

被引：37

作者：

Yang, Xia-wei ^{[1
,2
]}

Zhu, Jing-chuan ^{[1
,2
]}

Nong, Zhi-sheng ^{[1
,2
]}

He, Dong ^{[1
,2
]}

Lai, Zhong-hong ^{[1
,2
]}

Liu, Ying ^{[3
]}

Liu, Fa-wei ^{[4
]}

机构：

[1] Harbin Inst Technol, Natl Key Lab Precis Hot Proc Met, Harbin 150001, Peoples R China

[2] Harbin Inst Technol, Sch Mat Sci & Engn, Harbin 150001, Peoples R China

[3] Beijing Hangxing Machine Mfg Co, Beijing 100013, Peoples R China

[4] Shenyang Aircraft Corp, Phys Test Ctr, Shenyang 110034, Peoples R China

来源：

TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA | 2013年 / 23卷 / 03期

关键词：

A357; alloy; mechanical properties; artificial neural network; heat treatment parameters; ALUMINUM; PARAMETERS;

D O I：

10.1016/S1003-6326(13)62530-3

中图分类号：

TF [冶金工业];

学科分类号：

0806 ;

摘要：

The workpieces of A357 alloy were routinely heat treated to the T6 state in order to gain an adequate mechanical property. The mechanical properties of these workpieces depend mainly on solid-solution temperature, solid-solution time, artificial aging temperature and artificial aging time. An artificial neural network (ANN) model with a back-propagation (BP) algorithm was used to predict mechanical properties of A357 alloy, and the effects of heat treatment processes on mechanical behavior of this alloy were studied. The results show that this BP model is able to predict the mechanical properties with a high accuracy. This model was used to reflect the influence of heat treatments on the mechanical properties of A357 alloy. Isograms of ultimate tensile strength and elongation were drawn in the same picture, which are very helpful to understand the relationship among aging parameters, ultimate tensile strength and elongation.

引用

页码：788 / 795

页数：8

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